Bat

examples/asr_librispeech/asr_config.py

@@ -37,7 +37,7 @@ class TrainConfig:
     enable_fsdp:bool = False
     low_cpu_fsdp:bool = False
     run_validation:bool = True
-    batch_size_training:int = 4
+    batch_size_training:int = 1
     batching_strategy:str = field(default="packing", metadata={
         "help":"alternative: padding"
     }) #

examples/asr_librispeech/finetune_asr.py

@@ -1,11 +1,11 @@
-from slam_llm.pipeline.finetune import main as train
-
 import hydra
 import logging
 from dataclasses import dataclass, field
 from omegaconf import DictConfig, ListConfig, OmegaConf
 from asr_config import ModelConfig, TrainConfig, DataConfig, LogConfig, FSDPConfig
 
+from slam_llm.pipeline.finetune import main as train
+
 @dataclass
 class RunConfig:
     dataset_config: DataConfig = field(default_factory=DataConfig)

examples/asr_librispeech/scripts/finetune_whisper_large_linear_vicuna_7b.sh

@@ -1,7 +1,6 @@
 #!/bin/bash
 # export PYTHONPATH=/root/whisper:$PYTHONPATH
-export PYTHONPATH=/root/fairseq:$PYTHONPATH
-export CUDA_VISIBLE_DEVICES=0,1
+export CUDA_VISIBLE_DEVICES=0
 export TOKENIZERS_PARALLELISM=false
 # export CUDA_LAUNCH_BLOCKING=1
 export OMP_NUM_THREADS=1
@@ -11,16 +10,16 @@ export OMP_NUM_THREADS=1
 # export NCCL_DEBUG_SUBSYS=ALL
 # export TORCH_DISTRIBUTED_DEBUG=INFO
 
-run_dir=/root/SLAM-LLM
+run_dir=/mnt/cloudstorfs/sjtu_home/zhisheng.zheng/SLAM-LLM
 cd $run_dir
 code_dir=examples/asr_librispeech
 
-speech_encoder_path=/nfs/maziyang.mzy/models/Whisper/large-v3.pt
-llm_path=/nfs/maziyang.mzy/models/vicuna-7b-v1.5
-train_data_path=/nfs/maziyang.mzy/data/librispeech/librispeech_train_960h.jsonl
-val_data_path=/nfs/maziyang.mzy/data/librispeech/librispeech_dev_other.jsonl
+speech_encoder_path=/mnt/cloudstorfs/sjtu_home/zhisheng.zheng/models/whisper/large-v3.pt
+llm_path=/mnt/cloudstorfs/sjtu_home/zhisheng.zheng/models/vicuna-7b-v1.5
+train_data_path=/mnt/cloudstorfs/sjtu_home/zhisheng.zheng/data/librispeech/librispeech_dev_other.jsonl
+val_data_path=/mnt/cloudstorfs/sjtu_home/zhisheng.zheng/data/librispeech/librispeech_dev_other.jsonl
 
-output_dir=/root/tmp/vicuna-7b-v1.5-librispeech-linear-steplrwarmupkeep1e-4-whisper-largev3-$(date +"%Y%m%d")
+output_dir=/mnt/cloudstorfs/sjtu_home/zhisheng.zheng/SLAM-LLM/outputs/vicuna-7b-v1.5-librispeech-linear-steplrwarmupkeep1e-4-whisper-largev3-$(date +"%Y%m%d")
 
 hydra_args="
 hydra.run.dir=$output_dir \
@@ -51,11 +50,12 @@ hydra.run.dir=$output_dir \
 ++train_config.num_workers_dataloader=2 \
 ++train_config.output_dir=$output_dir \
 ++metric=acc \
+++log_config.log_file=$output_dir/log.txt \
 "
 
 # -m debugpy --listen 5678 --wait-for-client
 if [[ $CUDA_VISIBLE_DEVICES != *","* ]]; then
-    python -m debugpy --listen 5678 --wait-for-client $code_dir/finetune_asr.py \
+    python -m debugpy --listen 55555 --wait-for-client $code_dir/finetune_asr.py \
         --config-path "conf" \
         --config-name "prompt.yaml" \
         $hydra_args

examples/seld_spatialsoundqa/README.md

@@ -0,0 +1,43 @@
+# <img src="assets/bat.png" alt="SELD_SpatialSoundQA" width="25" height="25"> SELD_SpatialSoundQA
+
+This repo hosts the code and models of "[BAT: Learning to Reason about Spatial Sounds with Large Language Models](https://arxiv.org/abs/2402.01591)" [ICML 2024 [bib](https://github.com/zszheng147/Spatial-AST#citation)].
+
+## Performance and checkpoints
+Encoder | Projector | PEFT | LLM
+|---|---|---|---|
+[Spatial-AST](https://huggingface.co/zhisheng01/Bat/blob/main/spatial-ast.pth) | Q-Former | adapter |[llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b) 
+
+## Data preparation
+You need to prepare the data jsonl in this format.
+```
+{"audio_id": "eval/audio/YI-HlrcP6Qg4", "reverb_id": "q9vSo1VnCiC/0.npy", "audio_id2": null, "reverb_id2": null, "question_id": 0, "question_type": "CLASSIFICATION", "question": "Enumerate the sound occurrences in the audio clip.", "answer": "accelerating, revving, vroom; car; vehicle"}
+...
+{"audio_id": "eval/audio/YZX2fVPmUidA", "reverb_id": "q9vSo1VnCiC/32.npy", "audio_id2": "eval/audio/YjNjUU01quLs", "reverb_id2": "q9vSo1VnCiC/31.npy", "question_id": 58, "question_type": "MIXUP_NONBINARY_DISTANCE", "question": "How far away is the sound of the banjo from the sound of the whack, thwack?", "answer": "2m"}
+```
+
+## Train a new model
+```bash
+bash examples/seld_spatialsoundqa/scripts/finetune_spatial-ast_linear_llama_2_7b.sh
+```
+
+## Decoding with checkpoints
+```bash
+bash examples/seld_spatialsoundqa/scripts/decode_spatial-ast_linear_llama_2_7b.sh
+```
+
+
+## TODO
+- [x] Decode with checkpoints
+- [ ] Upload SpatialSoundQA dataset
+- [ ] Upload pretrained checkpoints
+- [ ] Update model performance
+
+## Citation
+```
+@article{zheng2024bat,
+  author    = {Zheng, Zhisheng and Peng, Puyuan and Ma, Ziyang and Chen, Xie and Choi, Eunsol and Harwath, David},
+  title     = {BAT: Learning to Reason about Spatial Sounds with Large Language Models},
+  journal   = {arXiv preprint arXiv:2402.01591},
+  year      = {2024},
+}
+```

examples/seld_spatialsoundqa/conf/ds_config.json

@@ -0,0 +1,19 @@
+{
+    "train_micro_batch_size_per_gpu": 4,
+    "gradient_accumulation_steps": 1,
+    "optimizer": {
+        "type": "Adam",
+        "params": {
+            "lr": 1e-4
+        }
+    },
+    "fp16": {
+        "enabled": true
+    },
+    "zero_optimization": {
+        "stage": 3,
+        "offload_optimizer": {
+            "device": "cpu"
+        }
+    }
+}

examples/seld_spatialsoundqa/finetune_seld.py

@@ -0,0 +1,45 @@
+import hydra
+import logging
+from dataclasses import dataclass, field
+from omegaconf import DictConfig, ListConfig, OmegaConf
+
+from seld_config import ModelConfig, TrainConfig, DataConfig, LogConfig, FSDPConfig, PeftConfig
+from slam_llm.pipeline.finetune import main as train
+
+@dataclass
+class RunConfig:
+    dataset_config: DataConfig = field(default_factory=DataConfig)
+    model_config: ModelConfig = field(default_factory=ModelConfig)
+    train_config: TrainConfig = field(default_factory=TrainConfig)
+    log_config: LogConfig = field(default_factory=LogConfig)
+    fsdp_config: FSDPConfig = field(default_factory=FSDPConfig)
+    peft_config: PeftConfig = field(default_factory=PeftConfig)
+    debug: bool = field(default=False, metadata={"help": "Use pdb when true"})
+    metric: str = field(default="acc", metadata={"help": "The metric for evaluation"})
+
+@hydra.main(config_name=None, version_base=None)
+def main_hydra(cfg: DictConfig):
+    run_config = RunConfig()
+    cfg = OmegaConf.merge(run_config, cfg)
+    def to_plain_list(cfg_item):
+        if isinstance(cfg_item, ListConfig):
+            return OmegaConf.to_container(cfg_item, resolve=True)
+        elif isinstance(cfg_item, DictConfig):
+            return {k: to_plain_list(v) for k, v in cfg_item.items()}
+        else:
+            return cfg_item
+
+    # kwargs = to_plain_list(cfg)
+    kwargs = cfg
+    log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
+    logging.basicConfig(level=log_level)
+
+    if kwargs.get("debug", False):
+        import pdb;
+        pdb.set_trace()
+
+    train(kwargs)
+
+
+if __name__ == "__main__":
+    main_hydra()

examples/seld_spatialsoundqa/inference_seld_batch.py

@@ -0,0 +1,53 @@
+import hydra
+import logging
+from dataclasses import dataclass, field
+from omegaconf import DictConfig, ListConfig, OmegaConf
+from typing import Optional
+
+from slam_llm.pipeline.inference_batch import main as inference
+from seld_config import ModelConfig, TrainConfig, DataConfig, LogConfig, FSDPConfig, PeftConfig
+
+@dataclass
+class RunConfig:
+    dataset_config: DataConfig = field(default_factory=DataConfig)
+    model_config: ModelConfig = field(default_factory=ModelConfig)
+    train_config: TrainConfig = field(default_factory=TrainConfig)
+    log_config: LogConfig = field(default_factory=LogConfig)
+    fsdp_config: FSDPConfig = field(default_factory=FSDPConfig)
+    peft_config: PeftConfig = field(default_factory=PeftConfig)
+    debug: bool = field(default=False, metadata={"help": "Use pdb when true"})
+    metric: str = field(default="acc", metadata={"help": "The metric for evaluation"})
+    decode_log: str = field(
+        default="output/decode_log",
+        metadata={"help": "The prefix for the decode output"},
+    )
+    ckpt_path: str = field(
+        default="output/model.pt", metadata={"help": "The path to projector checkpoint"}
+    )
+    peft_ckpt: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The path to peft checkpoint, should be a directory including adapter_config.json"
+        },
+    )
+
+
+@hydra.main(config_name=None, version_base=None)
+def main_hydra(cfg: DictConfig):
+    run_config = RunConfig()
+    cfg = OmegaConf.merge(run_config, cfg)
+    # kwargs = to_plain_list(cfg)
+    log_level = getattr(logging, cfg.get("log_level", "INFO").upper())
+
+    logging.basicConfig(level=log_level)
+
+    if cfg.get("debug", False):
+        import pdb
+
+        pdb.set_trace()
+
+    inference(cfg)
+
+
+if __name__ == "__main__":
+    main_hydra()

examples/seld_spatialsoundqa/model/slam_model_seld.py

@@ -0,0 +1,154 @@
+import torch
+import os
+import logging
+from slam_llm.models.slam_model import (
+    slam_model,
+    setup_tokenizer,
+    setup_encoder,
+    setup_encoder_projector,
+    setup_llm,
+)
+from slam_llm.utils.train_utils import print_model_size
+
+logger = logging.getLogger(__name__)
+
+def model_factory(train_config, model_config, **kwargs):
+    # return necessary components for training
+    tokenizer = setup_tokenizer(train_config, model_config, **kwargs)
+
+    encoder = setup_encoder(train_config, model_config, **kwargs)
+
+    # llm
+    llm = setup_llm(train_config, model_config, **kwargs)
+
+    # projector
+    encoder_projector = setup_encoder_projector(
+        train_config, model_config, **kwargs
+    )
+    model = slam_model_seld(
+        encoder,
+        llm,
+        encoder_projector,
+        tokenizer,
+        train_config,
+        model_config,
+        **kwargs,
+    )
+
+    ckpt_path = kwargs.get(
+        "ckpt_path", None
+    )  # FIX(MZY): load model ckpt(mainly projector, related to model_checkpointing/checkpoint_handler.py: save_model_checkpoint_peft)
+    if ckpt_path is not None:
+        logger.info("loading other parts from: {}".format(ckpt_path))
+        ckpt_dict = torch.load(ckpt_path, map_location="cpu")
+        model.load_state_dict(ckpt_dict, strict=False)
+
+    print_model_size(
+        model,
+        train_config,
+        (
+            int(os.environ["RANK"])
+            if train_config.enable_fsdp or train_config.enable_ddp
+            else 0
+        ),
+    )
+    return model, tokenizer
+
+
+class slam_model_seld(slam_model):
+    def __init__(
+        self,
+        encoder,
+        llm,
+        encoder_projector,
+        tokenizer,
+        train_config,
+        model_config,
+        **kwargs,
+    ):
+        super().__init__(
+            encoder,
+            llm,
+            encoder_projector,
+            tokenizer,
+            train_config,
+            model_config,
+            **kwargs,
+        )
+
+    @torch.no_grad()
+    def inference(
+        self,
+        wav_path=None,
+        prompt=None,
+        generation_config=None,
+        logits_processor=None,
+        stopping_criteria=None,
+        prefix_allowed_tokens_fn=None,
+        synced_gpus=None,
+        assistant_model=None,
+        streamer=None,
+        negative_prompt_ids=None,
+        negative_prompt_attention_mask=None,
+        **kwargs,
+    ):
+        # inference for asr model
+
+        device = kwargs.get("device", "cuda")
+        if os.path.exists(wav_path):  # Audio-Text QA
+            import whisper
+
+            audio_raw = whisper.load_audio(wav_path)
+            audio_raw = whisper.pad_or_trim(audio_raw)
+
+            mel_size = getattr(
+                self.dataset_config, "mel_size", 80
+            )  # 80 for large v1 and v2, 128 for large v3
+            audio_mel = (
+                whisper.log_mel_spectrogram(audio_raw, n_mels=mel_size)
+                .permute(1, 0)[None, :, :]
+                .to(device)
+            )
+
+            encoder_outs = self.encoder.extract_variable_length_features(
+                audio_mel.permute(0, 2, 1)
+            )
+
+            if self.model_config.encoder_projector == "q-former":
+                audio_mel_post_mask = torch.ones(
+                    encoder_outs.size()[:-1], dtype=torch.long
+                ).to(encoder_outs.device)
+                encoder_outs = self.encoder_projector(encoder_outs, audio_mel_post_mask)
+            if self.model_config.encoder_projector == "linear":
+                encoder_outs = self.encoder_projector(encoder_outs)
+        else:  # Text QA
+            encoder_outs = torch.empty(
+                1, 0, self.llm.model.embed_tokens.embedding_dim
+            ).to(device)
+
+        prompt = "USER: {}\n ASSISTANT:".format(prompt)
+        prompt_ids = self.tokenizer.encode(prompt)
+        prompt_length = len(prompt_ids)
+        prompt_ids = torch.tensor(prompt_ids, dtype=torch.int64).to(device)
+
+        if hasattr(self.llm.model, "embed_tokens"):
+            inputs_embeds = self.llm.model.embed_tokens(prompt_ids)
+        elif hasattr(self.llm.model.model, "embed_tokens"):
+            inputs_embeds = self.llm.model.model.embed_tokens(prompt_ids)
+        else:
+            inputs_embeds = self.llm.model.model.model.embed_tokens(prompt_ids)
+
+        inputs_embeds = torch.cat(
+            (encoder_outs, inputs_embeds[None, :, :]), dim=1
+        )  # [audio,prompt]
+
+        attention_mask = torch.ones(inputs_embeds.size()[:-1], dtype=torch.long).to(
+            inputs_embeds.device
+        )
+
+        # generate
+        model_outputs = self.generate(
+            inputs_embeds=inputs_embeds, attention_mask=attention_mask, **kwargs
+        )
+
+        return model_outputs